The dominant model for advanced digital photography is the digital single lens reflex (D-SLR) camera. In the main, most D-SLR cameras are organized to work within one paradigm. Film-based SLR cameras operate by using a lens apparatus connected to a camera body. When a shutter button is depressed, a microprocessor in the camera activates a shutter in the camera and an aperture in the lens to capture light onto a plane of film after a mirror flips up exposing the film. The silver-halide-based film is then chemically developed and images are preserved.
In a D-SLR, when the shutter button is depressed, a microprocessor (or SoC) in the camera activates a shutter in the camera and an aperture in the lens to capture light onto a digital sensor after a mirror flips up exposing the digital sensor. The sensor is typically either a charge coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) circuit that converts light to electrons. Once the sensor is exposed to light from the lens, camera circuitry moves the data from the sensor to a digital signal processor (DSP). The DSP performs a set of functions that filter the digital image file and transfers the converted data file to camera circuitry that stores and displays the corrected image file. A microprocessor (or SoC), which accesses a database in the camera, controls the image exposure settings, the internal camera circuitry and the mechanical operations of the shutter. In some cases, the camera microprocessor circuitry provides feedback to a microprocessor in the lens in order to measure and control the lens aperture and to synchronize exposure information between the lens aperture and the camera shutter. The user is able to manipulate the lens aperture, the camera shutter speed, the camera ISO speed, the data compression, and, in some cases, artificial light (such as a flash). The camera circuitry converts an analog image to digital format and converts the digital file to an analog image for presentation.
The field of image compression is divided into still and video.
Digital image and video compression algorithms solve complex optimization problems involving bandwidth and storage availability. As digital image and video files get larger, the problem of compression becomes more important.
Algorithms for compressing audio files use techniques for eliminating redundant or un-audible audio components. For instance, eliminating the very high and very low bandwidth audio signals allows the compression of a significant amount of total file size.
Digital image and video files, however, have complex specifications beyond only ocular perception limits. Compressing these file types requires implementation of a set of efficient algorithms. Restoration of compressed digital image and video files bit for bit involves lossless decompression and the exact compression of image and video information. Lossy compression, on the other hand, permanently eliminates a portion of the digital image or video file, which cannot be fully extracted upon decompression.
The two traditional models for compressing an image file involve (a) removing repetitive colors or (b) removing pixels. Removal of image details is typically performed on a set of pixels. A fast Fourier transform (FFT) is used to perform these operations. These techniques—embodied on JPEG standards—remove critical detail which is permanently lost. The effect of these traditional techniques is to compress images from twenty percent to ninety nine percent; these lossy approaches compromise image detail upon decompression. On the other hand, the Lempel-Ziv-Welch (LZW) algorithm is a lossless data compression technique applied to digital images.
As digital image and video file sizes increase relative to sensor size growth rates, it is important to develop a quality lossless compression-decompression (codec) algorithm.
The challenges presented include how to efficiently compress and decompress digital and video images in a lossless and scalable way and how to manage digital image storage and retrieval.